SoroushVahidi/scidocs huggingface dataset:
3D deep convolutional neural networks for amino acid environment similarity analysis
(2017)

Central to protein biology is the understanding of how structural elements give rise to observed function. The surfeit of protein structural data enables development of computational methods to systematically derive rules governing structural-functional relationships. However, performance of these methods depends critically on the choice of protein structural representation. Most current methods rely on features that are manually selected based on knowledge about protein structures. These are often general-purpose but not optimized for the specific application of interest. In this paper, we present a general framework that applies 3D convolutional neural network (3DCNN) technology to structure-based protein analysis. The framework automatically extracts task-specific features from the raw atom distribution, driven by supervised labels. As a pilot study, we use our network to analyze local protein microenvironments surrounding the 20 amino acids, and predict the amino acids most compatible with environments within a protein structure. To further validate the power of our method, we construct two amino acid substitution matrices from the prediction statistics and use them to predict effects of mutations in T4 lysozyme structures. Our deep 3DCNN achieves a two-fold increase in prediction accuracy compared to models that employ conventional hand-engineered features and successfully recapitulates known information about similar and different microenvironments. Models built from our predictions and substitution matrices achieve an 85% accuracy predicting outcomes of the T4 lysozyme mutation variants. Our substitution matrices contain rich information relevant to mutation analysis compared to well-established substitution matrices. Finally, we present a visualization method to inspect the individual contributions of each atom to the classification decisions. End-to-end trained deep learning networks consistently outperform methods using hand-engineered features, suggesting that the 3DCNN framework is well suited for analysis of protein microenvironments and may be useful for other protein structural analyses.
Wireless Technologies for IoT in Smart Cities
(2018)

As cities continue to grow, numerous initiatives for Smart Cities are being conducted. The concept of Smart City encompasses several concepts being governance, economy, management, infrastructure, technology and people. This means that a Smart City can have different communication needs. Wireless technologies such as WiFi, ZigBee, Bluetooth, WiMax, 4G or LTE (Long Term Evolution) have presented themselves as solutions to the communication needs of Smart City initiatives. However, as most of them employ unlicensed bands, interference and coexistence problems are increasing. In this paper, the wireless technologies available nowadays for IoT (Internet of Things) in Smart Cities are presented. Our contribution is a review of wireless technologies, their comparison and the problems that difficult coexistence among them. In order to do so, the characteristics and adequacy of wireless technologies to each domain are considered. The problems derived of over-crowded unlicensed spectrum and coexistence difficulties among each technology are discussed as well. Finally, power consumption concerns are addressed.
Chinese Image Text Recognition on grayscale pixels
(2014)

This paper presents a novel scheme for Chinese text recognition in images and videos. It's different from traditional paradigms that binarize text images, fed the binarized text to an OCR engine and get the recognized results. The proposed scheme, named grayscale based Chinese Image Text Recognition (gCITR), implements the recognition directly on grayscale pixels via the following steps: image text over-segmentation, building recognition graph, Chinese character recognition and beam search determination. The advantages of gCITR lie in: (1) it does not heavily rely on the performance of binarization, which is not robust in practical and thus severely affects the performance of OCR, (2) grayscale image retains more information of the text thus facilitates the recognition. Experimental results on text from 13 TV news videos demonstrate the effectiveness of the proposed gCITR, from which significant performance gains are observed.
Applying Stochastic Integer Programming to Optimization of Resource Scheduling in Cloud Computing
(2012)

Resource scheduling based on SLA(Service Level Agreement) in cloud computing is NP-hard problem. There is no efficient method to solve it. This paper proposes an optimal model for the problem and give an algorithm by applying stochastic integer programming technique. Applying Gröbner bases theory for solving the stochastic integer programming problem and the experimental results of the implementation are also presented.
ZooKeeper: Wait-free Coordination for Internet-scale Systems
(2010)

In this paper, we describe ZooKeeper, a service for coordinating processes of distributed applications. Since ZooKeeper is part of critical infrastructure, ZooKeeper aims to provide a simple and high performance kernel for building more complex coordination primitives at the client. It incorporates elements from group messaging, shared registers, and distributed lock services in a replicated, centralized service. The interface exposed by ZooKeeper has the wait-free aspects of shared registers with an event-driven mechanism similar to cache invalidations of distributed file systems to provide a simple, yet powerful coordination service. The ZooKeeper interface enables a high-performance service implementation. In addition to the wait-free property, ZooKeeper provides a per client guarantee of FIFO execution of requests and linearizability for all requests that change the ZooKeeper state. These design decisions enable the implementation of a high performance processing pipeline with read requests being satisfied by local servers. We show for the target workloads, 2:1 to 100:1 read to write ratio, that ZooKeeper can handle tens to hundreds of thousands of transactions per second. This performance allows ZooKeeper to be used extensively by client applications.
Performance Analysis of Enhanced SPECK Algorithm
(2018)

A secure lightweight block cipher is an effective security solution for applications running on resource-constrained devices. The SPECK family of lightweight block ciphers is designed for low-end devices that need data security. The randomness of the keystream produced by this algorithm is a good indicator of its security strength. Nevertheless, the computed proportion of sequences based on empirical test results falls beyond the range of acceptable interval, an evidence of non-randomness to each algorithm. In this study, the researchers enhanced SPECK (k-SPECK) through the addition of a key derivation function in its processes. Both SPECK and k - SPECK were evaluated with the two sophisticated statistical test suite. The result shows that k - SPECK outperformed SPECK in terms of randomness as its three variations successfully passed all the test of statistical packages NIST and DieHarder, making it more cryptographically secured. Similarly, software implementation was also evaluated showing that k - SPECK 128/192 and 128/256 performed faster in encryption cycle compared to SPECK.
FPGA Implementations of the Round Two SHA-3 Candidates
(2010)

The second round of the NIST-run public competition is underway to find a new hash algorithm(s) for inclusion in the NIST Secure Hash Standard (SHA-3). This paper presents the full implementations of all of the second round candidates in hardware with all of their variants. In order to determine their computational efficiency, an important aspect in NIST's round two evaluation criteria, this paper gives an area/speed comparison of each design both with and without a hardware interface, thereby giving an overall impression of their performance in resource constrained and resource abundant environments. The implementation results are provided for a Virtex-5 FPGA device. The efficiency of the architectures for the hash functions are compared in terms of throughput per unit area.
PC-01: Introduction to computational thinking: Educational technology in primary and secondary education
(2016)

This article describes the design and implementation of the course Introduction to Computational Thinking (PC-01) for primary and secondary education. The course introduces the “concepts” and core “processes” of Computational Thinking using a visual programming environment. The design of the course PC-01 includes a set of multimedia content, learning tools and technologies that help teachers and students in the teaching-learning process. These tools allow the teacher the successful delivery of the course content as well as the individual/collective monitoring of students' progress in the course. Students have access to their progress throughout the course and participate in creative and collaborative tasks. This course also serves to introduce transversely educational technologies to many students of primary and secondary education. The technological environment integrates the online teaching resources and the methodological tools of the course. The course uses Scratch 2.0 as the programming environment and Moodle as the learning platform. At present the course is being implemented in public schools in the Dominican Republic.
Soft Switching Circuit for Interleaved Boost Converters
(2007)

A zero-voltage switching-zero-current switching interleaved boost converter is proposed in this paper. An active circuit branch in parallel with the main switches is added and it is composed of an auxiliary switch and a snubber capacitor. By using this interleaved converter topology, zero current turn-on and zero voltage turn-off of the main switches can be achieved and the reverse-recovery loss of boost diode can be reduced. In addition, the auxiliary switches are zero-voltage transmission during the whole switching transition. A prototype of boost converter rated at 1.2kW has been built to confirm the effectiveness of the converter
Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine
(2010)

Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.
An End-to-End Multitask Learning Model for Fact Checking
(2018)

With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as “supports”, “refutes” or “not enough info” with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.
OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems
(2010)

In this paper, we present an approach for modeling 3D environments based on octrees using a probabilistic occupancy estimation. Our technique is able to represent full 3D models including free and unknown areas. It is available as an open-source library to facilitate the development of 3D mapping systems. We also provide a detailed review of existing approaches to 3D modeling. Our approach was thoroughly evaluated using different real-world and simulated datasets. The results demonstrate that our approach is able to model the data probabilistically while, at the same time, keeping the memory requirement at a minimum.
Proximal Tibiofibular Joint Instability and Treatment Approaches: A Systematic Review of the Literature.
(2017)

PURPOSE
To evaluate the treatment options, outcomes, and complications associated with proximal tibiofibular joint (PTFJ) instability, which will aim to improve surgical treatment of PTFJ instability and aid surgeons in their decision making and treatment selection.


METHODS
A systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Inclusion criteria were as follows: PTFJ instability treatment techniques, PTFJ surgical outcomes, English language, and human studies. Exclusion criteria were cadaveric studies, animal studies, basic science articles, editorial articles, review articles, and surveys. Furthermore, we excluded studies that did not report patient follow-up time and studies without any patient-reported, clinical or radiographic outcomes at the final follow-up.


RESULTS
The systematic review identified 44 studies (96 patients) after inclusion and exclusion criteria application. For the treatment of PTFJ instability, there were 18 studies (35 patients) describing nonoperative management, 3 studies (4 patients) reported on open reduction, 11 studies (25 patients) reported on fixation, 4 studies (10 patients) that described proximal fibula resection, 3 studies (11 patients) reported on adjustable cortical button repair, 2 studies (3 patients) reported on ligament reconstructions, and 5 (8 patients) studies reported on biceps femoris tendon rerouting. The most (77% to 90%) PTFJ dislocations and instability were anterolateral/unspecified anterior dislocation or instability. Improved outcomes after all forms of PTFJ instability treatment were reported; however, high complication rates were associated with both PTFJ fixation (28%) and fibular head resection (20%).


CONCLUSIONS
Improved outcomes can be expected after surgical treatment of PTFJ instability. Proximal tibiofibular ligament reconstruction, specifically biceps rerouting and anatomic graft reconstruction, leads to improved outcomes with low complication rates. Nonoperative treatment is associated with persistent symptoms, whereas both fixation and fibular head resection are associated with high complication rates.


LEVEL OF EVIDENCE
Level IV, systematic review of level IV studies.
Knowledge Graph Embedding with Numeric Attributes of Entities
(2018)

Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities’ numeric attributes in KGs. In this paper, we propose a new KG embedding model which jointly model entity relations and numeric attributes. Our approach combines an attribute embedding model with a translation-based structure embedding model, which learns the embeddings of entities, relations, and attributes simultaneously. Experiments of link prediction on YAGO and Freebase show that the performance is effectively improved by adding entities’ numeric attributes in the embedding model.
Improving Distributional Similarity with Lessons Learned from Word Embeddings
(2015)

Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distributional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves. Furthermore, we show that these modifications can be transferred to traditional distributional models, yielding similar gains. In contrast to prior reports, we observe mostly local or insignificant performance differences between the methods, with no global advantage to any single approach over the others.
Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy.
(1993)

A dual taxonomy is presented to reconcile 2 incongruous facts about antisocial behavior: (a) It shows impressive continuity over age, but (b) its prevalence changes dramatically over age, increasing almost 10-fold temporarily during adolescence. This article suggests that delinquency conceals 2 distinct categories of individuals, each with a unique natural history and etiology: A small group engages in antisocial behavior of 1 sort or another at every life stage, whereas a larger group is antisocial only during adolescence. According to the theory of life-course-persistent antisocial behavior, children's neuropsychological problems interact cumulatively with their criminogenic environments across development, culminating in a pathological personality. According to the theory of adolescence-limited antisocial behavior, a contemporary maturity gap encourages teens to mimic antisocial behavior in ways that are normative and adjustive.
A hot/cool-system analysis of delay of gratification: dynamics of willpower.
(1999)

A 2-system framework is proposed for understanding the processes that enable--and undermine--self-control or "willpower" as exemplified in the delay of gratification paradigm. A cool, cognitive "know" system and a hot, emotional "go" system are postulated. The cool system is cognitive, emotionally neutral, contemplative, flexible, integrated, coherent, spatiotemporal, slow, episodic, and strategic. It is the seat of self-regulation and self-control. The hot system is the basis of emotionality, fears as well as passions--impulsive and reflexive--initially controlled by innate releasing stimuli (and, thus, literally under "stimulus control"): it is fundamental for emotional (classical) conditioning and undermines efforts at self-control. The balance between the hot and cool systems is determined by stress, developmental level, and the individual's self-regulatory dynamics. The interactions between these systems allow explanation of findings on willpower from 3 decades of research.
Adolescent sexual risk behavior: a multi-system perspective.
(2001)

Adolescents are at high risk for a number of negative health consequences associated with early and unsafe sexual activity, including infection with human immunodeficiency virus, other sexually transmitted diseases, and unintended pregnancy. As a result, researchers have attempted to identify those factors that influence adolescent sexual risk behavior so that meaningful prevention and intervention programs may be developed. We propose that research efforts so far have been hampered by the adoption of models and perspectives that are narrow and do not adequately capture the complexity associated with the adolescent sexual experience. In this article, we review the recent literature (i.e., 1990-1999) pertaining to the correlates of adolescent sexual risk-taking, and organize the findings into a multisystemic perspective. Factors from the self, family, and extrafamilial systems of influence are discussed. We also consider several methodological problems that limit the literature's current scope, and consider implications of the adoption of a multisystemic framework for future research endeavors. We conclude with a discussion of the implications of the available research for practitioners working to reduce sexual risk behavior among adolescents.
Separation of Fine Granular Mixtures in S-Plate-Type Electrostatic Separators
(2007)

Plate-type electrostatic separators are commonly used for the selective sorting of conductive from nonconductive constituents of a granular mixture. This paper addresses the more delicate issue of granular materials that are characterized by finite nonzero resistivities. The study was conducted with three materials having similar granule size range and specific weight but slightly different electrical properties. Two sets of experiments were performed for evaluating the effect of electrode geometry on the behavior of each of the materials. These results were then employed for predicting the outcome of the electrostatic separation of binary mixtures of good and poor conductors. A third set of experiments validated the predictions and pointed out a couple of nonelectric factors that might deteriorate the performance of a plate-type electrostatic separator in an industrial environment.
Design of Permanent Magnet-Assisted Synchronous Reluctance Motors Made Easy
(2007)

Electric motor design is a multi-variable problem which involves geometric dimensions of the stator and rotor. Presenting a unique solution for a family of optimization criteria has always been a challenge for motor designers. Several numerical tools such as finite element methods (FEM) have been developed to perform a precise analysis and predict the outcome of the design. However, limits in parametric analysis as well as mathematical and computational burden on numerical tools usually prohibit the designer in obtaining a unique solution for the design problem. These limits and demands in optimized solutions motivate the designer to use analytical models in order to perform a comprehensive parametric design. An accurate analytical model is crucial for this purpose. In this paper, an analytical model for permanent magnet assisted synchronous reluctance motor (PMa- SynRM) with four flux barriers and one cutout per pole is developed. Flux densities are found in the air gap, in the cutouts, and in the flux barriers; thus, the back-EMF developed by the permanent magnets is derived. Equations for the d-axis and the q-axis inductances are also obtained. Electromagnetic torque is finally derived using the co-energy method. The developed analytical model highlights the contribution of the reluctance variation and permanent magnets on the developed torque. Simulation results are obtained using both Matlab and Ansoft/Maxwell packages. These outcomes are supported by the experimental results obtained from a laboratory test bed.
Effective Value of Decision Tree with KDD 99 Intrusion Detection Datasets for Intrusion Detection System
(2008)

A decision tree is a outstanding method for the data mining. In intrusion detection systems (IDSs), the data mining techniques are useful to detect the attack especially in anomaly detection. For the decision tree, we use the DARPA 98 Lincoln Laboratory Evaluation Data Set (DARPA Set) as the training data set and the testing data set. KDD 99 Intrusion Detection data set is also based on the DARPA Set. These three entities are widely used in IDSs. Hence, we describe the total process to generate the decision tree learned from the DARPA Sets. In this paper, we also evaluate the effective value of the decision tree as the data mining method for the IDSs, and the DARPA Set as the learning data set for the decision trees.